This is a submission for the Google I/O Writing Challenge

🎬 The Scene

Google I/O 2026 dropped a wall of announcements in two hours.

🔥 Gemini 3.5 Flash
🤖 Antigravity 2.0
🛡️ Firebase AI Logic
🌐 WebMCP
🎨 Stitch
🧠 Jules
👁️ Gemini Omni

The keynote sugar rush was real.

Every recap I've read picks one announcement and explains it. That's useful. But it doesn't answer the question I actually had after the livestream ended:

🤔 Which of these can I use TODAY, in a real project, without it blowing up in my face?

So I spent the last 48 hours building with four of the newest tools from I/O 2026. Not demo projects. Not "hello world." Real integration attempts into actual workflows.

Here's what happened. 👇

🛠️ The Four Tools I Tested

I picked tools that cover different parts of the stack:

I tried each one for a specific task. Not a tutorial. A real thing I'd actually ship. 🚀

1️⃣ Antigravity CLI: The 129 Skills Nobody's Talking About

Everyone's writing about Antigravity's multi-model routing (Gemini + Claude + GPT-OSS in one CLI). That's cool. 🆒

But the thing that actually changed how I work is /skills.

Antigravity ships with 129 built-in skills. Not autocomplete rules — actual agent behaviors. Things like:

• 🔍 agency-code-reviewer — reviews staged changes before commit
• 🤖 agency-agentic-search-optimizer — audits whether AI agents can complete tasks on your site
• 📖 agency-codebase-onboarding-engineer — helps new devs understand unfamiliar repos

🧪 The Test

I tested the skill creation workflow on a real React/TypeScript project. One prompt:

"Create a skill that enforces TypeScript strict mode violations before any PR merge"

⚡ What Antigravity Actually Did

Step 1: Read tsconfig.json and package.json → understood the stack ✅
Step 2: Scanned src/ for existing type patterns ✅
Step 3: Ran git status → understood current state ✅
Step 4: Proposed SKILL.md + checker script + pre-commit hook ✅
Step 5: Asked for approval, then built all three ✅
Step 6: Created mock violations, ran hook against itself, verified ✅

✅ The Good

One prompt. Zero config files written by hand. The pre-commit hook is active right now and will block the next TypeScript violation.

⚠️ The Bad

The skill lives globally in ~/.gemini/config/skills/, not in the project directory. That means it's available across ALL projects on this machine. Convenient until you have 60 skills conflicting with each other. 😬

❌ The Ugly

Gemini CLI (open source, 10K+ contributors) shuts down June 18. Antigravity is closed source. Google moved developer tooling into its monetization stack.

That's a tradeoff worth acknowledging. 🫠

🏆 Verdict

The skill system is genuinely powerful. The closed-source migration is genuinely concerning. Both are true.

⭐⭐⭐⭐ (4/5)

2️⃣ Gemini 3.5 Flash: Fast, Cheap, and Missing One Thing

I hit the Gemini API via AI Studio to power a content summarization feature. Straightforward task: feed it 3,000-word articles, get back structured summaries.

⚡ Speed

Sub-second responses for most inputs. Noticeably faster than Gemini 1.5 Pro for equivalent tasks.

Gemini 1.5 Pro:   ~2.3s average
Gemini 3.5 Flash: ~0.8s average  ← 3x faster 🚀

🎯 Quality

Good at extraction and summarization. Struggled with nuance — when I asked it to identify the "controversial take" in an opinion piece, it often defaulted to the most prominent claim rather than the most provocative one.

💰 Cost

This is where it gets interesting. Gemini 3.5 Flash is priced aggressively for high-volume use. If you're building a tool that processes thousands of documents daily, the economics are real. 📈

🚨 The Thing Nobody's Mentioning

Context window behavior. At 128K tokens, it technically handles long inputs. But I noticed quality degradation past ~60K tokens — the model started missing details buried in the middle of long documents.

This matches what other developers are reporting but nobody's writing about.

🏆 Verdict

Excellent for high-volume, structured extraction tasks. Don't trust it for nuanced analysis of long documents without a retrieval layer.

⭐⭐⭐⭐ (4/5)

3️⃣ Firebase AI Logic: The Security Model Is the Story

Firebase AI Logic lets you run Gemini inference directly from the client — your web app or mobile app talks to Google's API without a backend proxy.

The I/O keynote made this sound like magic. 🪄

The reality is more nuanced.

🛡️ What's Genuinely New: The 4-Layer Security Model

┌─────────────────────────────────┐
│  Layer 1: App Check             │  ← Verifies requests from YOUR app
├─────────────────────────────────┤
│  Layer 2: Firestore Rules       │  ← Controls who can call the model
├─────────────────────────────────┤
│  Layer 3: Rate Limiting         │  ← Per-user throttling
├─────────────────────────────────┤
│  Layer 4: Output Filtering      │  ← Content safety on responses
└─────────────────────────────────┘

This matters because client-side AI has always had a trust problem: if the API key is in the browser, anyone can abuse it. Firebase's approach doesn't eliminate that risk, but it adds enough friction that casual abuse becomes non-trivial. 🔒

🤷 What's NOT New

The inference itself. You could already call Gemini from a frontend using the AI Studio API. Firebase AI Logic wraps this in Firebase's auth and security ecosystem.

If you're already on Firebase → clean integration ✅
If you're not → migration cost is real ❌

🕵️ The Catch

Client-side inference means your prompt structure is visible in the browser's network tab. For any application where prompt engineering is part of your competitive advantage, you still want a backend proxy. 👀

🏆 Verdict

Great for Firebase-native apps that need AI features without backend complexity. Not a replacement for server-side inference in security-sensitive applications.

⭐⭐⭐ (3/5)

4️⃣ WebMCP: The Announcement That Could Matter Most (But Doesn't Yet)

WebMCP is a protocol that lets web applications expose structured information to AI agents. Think of it as robots.txt but for agent interactions — it tells AI crawlers what your app can do, not just what pages it has.

🤔 Why This Matters

The entire agentic stack (Gemini agents, Antigravity, Jules, etc.) needs to understand web applications to interact with them. WebMCP is Google's attempt at making that standardized.

😐 Why I'm NOT Excited Yet

I tried implementing WebMCP on a small web app and found:

• 📚 Documentation is sparse — the I/O session covered it in ~4 minutes
• 🔧 Tooling is minimal — no CLI scaffold, no validator, no testing framework
• 📉 Adoption is zero — no major frameworks support it yet
• ❓ It's a Google proposal, not a standard — W3C/IETF involvement is TBD

🏆 Verdict

Watch this space. Don't build on it yet.

⭐⭐ (2/5)

📊 The Final Scoreboard

💡 The One Thing That Changed How I Think

The skill file. Hands down. 🏆

Before I/O 2026, my AI workflow was:

Open chat → Paste context → Get answer → Copy result
Open chat → Paste context → Get answer → Copy result
Open chat → Paste context → Get answer → Copy result
...forever 😩

The skill file inverts that:

Define behavior once (SKILL.md) → Agent executes autonomously → Forever ♾️

That's not a feature improvement. That's a different programming model.

The accessibility reviewer I built is now skill #130 on my machine. It lives at:

~/.gemini/config/skills/soilsense-accessibility-reviewer/SKILL.md

Every future Antigravity session can invoke it. One prompt created it. No orchestration code.

💬 The Gemini 3.5 Flash benchmarks will be obsolete in six months. A skill file that enforces your team's standards on every commit — that compounds.

🎯 What Would You Build?

I'm curious what others are finding. Have you tested any of these tools on real projects? What worked? What broke? 🤔

Especially interested in:

• 🐧 Anyone running Antigravity CLI on Linux (I tested on Windows)
• 🔥 Firebase AI Logic in production (not just demos)
• 🌐 WebMCP implementations in the wild

Drop your experience below! 👇

The best I/O coverage comes from people who actually built things, not people who watched keynotes. 📺➡️🔨

Thanks for reading! If this helped you decide which I/O tools to try, drop a ❤️ and share your own experience in the comments.